Important announcements:

In the past, this course has filled up. If this happens, please do not email me to request registration. Enrollment closes when the room is beyond capacity. Watch the registration list closely until the add/drop deadline.

I will not be using the classroom recording system. Students registered in the course should plan on attending every class.

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Description

The course will cover selected topics and new developments in data mining and applied machine learning, with a particular emphasis on good methods and practices for effective deployment of real systems. We will study commonly used algorithms and techniques, including linear and logistic regression, clustering, neural networks, support vector machines, decision trees and more. We will also discuss methods to address practical issues such as feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large datasets.
Important note:Students who took COMP-652 in 2013 or before CANNOT take COMP-551. Students who took COMP-652 in Winter 2014 or after (or intend to take it) can take COMP-551. Contents of both courses have been designed to avoid too much overlap. COMP-551 focuses on the practical application of machine learning, whereas COMP-652 (starting in Winter 2014) focuses on theoretical analysis of machine learning, reinforcement learning, bandits and analysis of time series.